Rebrickable analysis

Libraries

library(ggplot2)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(skimr)
library(png)
library(grid)
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

Load data

load_csv <- function(csv_name, data_folder = 'data') {
  df <- read.csv(file.path(data_folder, csv_name))
  as_tibble(df)
}

inventories_df <- load_csv('inventories.csv')
inventory_sets_df <- load_csv('inventory_sets.csv')
sets_df <- load_csv('sets.csv')
themes_df <- load_csv('themes.csv')
inventory_minifigs_df <- load_csv('inventory_minifigs.csv')
minifigs_df <- load_csv('minifigs.csv')
inventory_parts_df <- load_csv('inventory_parts.csv')
colors_df <- load_csv('colors.csv')
parts_df <- load_csv('parts.csv')
elements_df <- load_csv('elements.csv')
part_categories_df <- load_csv('part_categories.csv')
part_relationships_df <- load_csv('part_relationships.csv')

Basic tables analysis

Data schema

Tables

Inventories

Data summary

id	version	set_num
1	1	7922-1
3	1	3931-1
4	1	6942-1
15	1	5158-1
16	1	903-1
17	1	850950-1

Name	inventories_df
Number of rows	37265
Number of columns	3
_______________________
Column type frequency:
character	1
numeric	2
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
set_num	0	1	3	20	0	35644	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
id	0	1	61103.60	51380.10	1	14424	54379	88842	194312	▇▆▂▂▂
version	0	1	1.09	0.58	1	1	1	1	16	▇▁▁▁▁

Inventory sets

Data summary

inventory_id	set_num	quantity
35	75911-1	1
35	75912-1	1
39	75048-1	1
39	75053-1	1
50	4515-1	1
50	4520-1	2

Name	inventory_sets_df
Number of rows	4358
Number of columns	3
_______________________
Column type frequency:
character	1
numeric	2
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
set_num	0	1	5	20	0	3171	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
inventory_id	0	1	52518.95	59063.13	35	8076	16423	98685	191576	▇▁▁▂▁
quantity	0	1	1.81	5.67	1	1	1	1	60	▇▁▁▁▁

Sets

Data summary

set_num	name	year	theme_id	num_parts	img_url
001-1	Gears	1965	1	43	https://cdn.rebrickable.com/media/sets/001-1.jpg
0011-2	Town Mini-Figures	1979	67	12	https://cdn.rebrickable.com/media/sets/0011-2.jpg
0011-3	Castle 2 for 1 Bonus Offer	1987	199	0	https://cdn.rebrickable.com/media/sets/0011-3.jpg
0012-1	Space Mini-Figures	1979	143	12	https://cdn.rebrickable.com/media/sets/0012-1.jpg
0013-1	Space Mini-Figures	1979	143	12	https://cdn.rebrickable.com/media/sets/0013-1.jpg
0014-1	Space Mini-Figures	1979	143	2	https://cdn.rebrickable.com/media/sets/0014-1.jpg

Name	sets_df
Number of rows	21880
Number of columns	6
_______________________
Column type frequency:
character	3
numeric	3
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
set_num	0	1	3	20	0	21880	0
name	0	1	2	93	0	18752	0
img_url	0	1	46	63	0	21880	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
year	0	1	2007.76	13.96	1949	2001	2012	2018	2024	▁▁▁▃▇
theme_id	0	1	441.97	215.53	1	273	497	608	752	▃▃▃▇▇
num_parts	0	1	161.38	418.14	0	3	31	139	11695	▇▁▁▁▁

Themes

Data summary

id	name	parent_id
1	Technic	NA
3	Competition	1
4	Expert Builder	1
16	RoboRiders	1
17	Speed Slammers	1
18	Star Wars	1

Name	themes_df
Number of rows	468
Number of columns	3
_______________________
Column type frequency:
character	1
numeric	2
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
name	0	1	2	42	0	385	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
id	0	1.00	433.46	216.55	1	250.5	466	625.25	752	▅▅▅▆▇
parent_id	145	0.69	360.64	197.19	1	186.0	411	512.50	697	▅▃▂▇▂

Inventory Minifigs

Data summary

inventory_id	fig_num	quantity
3	fig-001549	1
4	fig-000764	1
19	fig-000555	1
25	fig-000574	1
26	fig-000842	1
26	fig-008641	1

Name	inventory_minifigs_df
Number of rows	20858
Number of columns	3
_______________________
Column type frequency:
character	1
numeric	2
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
fig_num	0	1	10	10	0	13455	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
inventory_id	0	1	43010.44	52256.78	3	7869	15681	66834	194312	▇▁▁▁▁
quantity	0	1	1.06	0.78	1	1	1	1	100	▇▁▁▁▁

Minifigs

Data summary

fig_num	name	num_parts	img_url
fig-000001	Toy Store Employee	4	https://cdn.rebrickable.com/media/sets/fig-000001.jpg
fig-000002	Customer Kid	4	https://cdn.rebrickable.com/media/sets/fig-000002.jpg
fig-000003	Assassin Droid, White	8	https://cdn.rebrickable.com/media/sets/fig-000003.jpg
fig-000004	Man, White Torso, Black Legs, Brown Hair	4	https://cdn.rebrickable.com/media/sets/fig-000004.jpg
fig-000005	Captain America with Short Legs	3	https://cdn.rebrickable.com/media/sets/fig-000005.jpg
fig-000006	Lloyd Avatar	5	https://cdn.rebrickable.com/media/sets/fig-000006.jpg

Name	minifigs_df
Number of rows	13764
Number of columns	4
_______________________
Column type frequency:
character	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
fig_num	0	1	10	10	0	13764	0
name	0	1	1	148	0	13354	0
img_url	0	1	53	53	0	13764	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
num_parts	0	1	5.3	6.03	0	4	4	5	156	▇▁▁▁▁

Inventory parts

Data summary

inventory_id	part_num	color_id	quantity	is_spare	img_url
1	48379c01	72	1	f	https://cdn.rebrickable.com/media/parts/photos/1/48379c01-1-e7daa845-2671-4737-8642-3b1574308155.jpg
1	48395	7	1	f	https://cdn.rebrickable.com/media/parts/photos/7/48395-7-b9152acf-2fa5-4836-a04d-5b7fd39c2406.jpg
1	stickerupn0077	9999	1	f
1	upn0342	0	1	f
1	upn0350	25	1	f
3	2343	47	1	f	https://cdn.rebrickable.com/media/parts/elements/3000240.jpg

Name	inventory_parts_df
Number of rows	1180987
Number of columns	6
_______________________
Column type frequency:
character	3
numeric	3
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
part_num	0	1	1	20	0	51051	0
is_spare	0	1	1	1	0	2	0
img_url	0	1	0	117	8180	74266	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
inventory_id	0	1	50849.46	55136.94	1	9404	22838	87088	194312	▇▂▁▂▁
color_id	0	1	131.78	862.38	-1	4	15	71	9999	▇▁▁▁▁
quantity	0	1	3.37	9.95	1	1	2	4	3064	▇▁▁▁▁

Colors

Data summary

id	name	rgb	is_trans
-1	[Unknown]	0033B2	f
0	Black	05131D	f
1	Blue	0055BF	f
2	Green	237841	f
3	Dark Turquoise	008F9B	f
4	Red	C91A09	f

Name	colors_df
Number of rows	263
Number of columns	4
_______________________
Column type frequency:
character	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
name	0	1	3	28	0	263	0
rgb	0	1	6	6	0	223	0
is_trans	0	1	1	1	0	2	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
id	0	1	651.38	750.55	-1	83	1005	1070.5	9999	▇▁▁▁▁

Parts

Data summary

part_num	name	part_cat_id	part_material
003381	Sticker Sheet for Set 663-1	58	Plastic
003383	Sticker Sheet for Sets 618-1, 628-2	58	Plastic
003402	Sticker Sheet for Sets 310-3, 311-1, 312-3	58	Plastic
003429	Sticker Sheet for Set 1550-1	58	Plastic
003432	Sticker Sheet for Sets 357-1, 355-1, 940-1	58	Plastic
003434	Sticker Sheet for Set 575-2, 653-1, 460-1	58	Plastic

Name	parts_df
Number of rows	52615
Number of columns	4
_______________________
Column type frequency:
character	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
part_num	0	1	1	20	0	52615	0
name	0	1	3	222	0	52103	0
part_material	0	1	4	16	0	7	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
part_cat_id	0	1	38.91	22.08	1	17	41	60	68	▃▃▂▁▇

Elements

Data summary

element_id	part_num	color_id	design_id
6443403	2277c01pr0009	1	2277
6300211	67906c01	14	67908
4566309	2564	0	2564
4275423	53657	1004	53657
6194308	92926	71	28967
6229123	26561	4	26561

Name	elements_df
Number of rows	84138
Number of columns	4
_______________________
Column type frequency:
character	1
numeric	3
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
part_num	0	1	2	19	0	33765	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
element_id	0	1.00	5222065.12	1596842.63	9327	4259773.50	6057754	6262024.5	61532443	▇▁▁▁▁
color_id	0	1.00	539.67	2044.86	-1	8.00	28	135.0	9999	▇▁▁▁▁
design_id	23682	0.72	45569.87	30750.66	1001	18453.75	41748	75474.5	107520	▇▆▅▅▃

Part Relationships

Data summary

rel_type	child_part_num	parent_part_num
P	3626cpr3662	3626c
P	87079pr9974	87079
P	3960pr9971	3960
R	98653pr0003	98086pr0003
R	98653pr0003	98088pat0003
R	98653pr0003	98089pat0003

Name	part_relationships_df
Number of rows	29977
Number of columns	3
_______________________
Column type frequency:
character	3
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
rel_type	0	1	1	1	0	6	0
child_part_num	0	1	1	20	0	27139	0
parent_part_num	0	1	1	19	0	4725	0

Part categories

Data summary

id	name
1	Baseplates
3	Bricks Sloped
4	Duplo, Quatro and Primo
5	Bricks Special
6	Bricks Wedged
7	Containers

Name	part_categories_df
Number of rows	66
Number of columns	2
_______________________
Column type frequency:
character	1
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
name	0	1	4	44	0	66	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
id	0	1	35.36	19.41	1	19.25	35.5	51.75	68	▇▇▇▇▇

Dataframes size

All rows in dataframes:  1446639 

All columns in dataframes:  45 

All values in dataframes:  8099232 

Analysis deep dive

Themes

Themes sum of rows with NA value

## [1] 145

All NA values are in parent_id column

## # A tibble: 1 × 3
##      id  name parent_id
##   <int> <int>     <int>
## 1     0     0       145

Create themes_parent_df

## # A tibble: 145 × 2
##       id name             
##    <int> <chr>            
##  1     1 Technic          
##  2    22 Creator          
##  3    34 Make & Create    
##  4    50 Town             
##  5    52 City             
##  6   112 Racers           
##  7   126 Space            
##  8   147 Pirates          
##  9   155 Modular Buildings
## 10   158 Star Wars        
## # ℹ 135 more rows

Join themes_parent_df with themes_df, insert name_child into name_parent if name_parent is NA

## # A tibble: 468 × 3
##       id name_child             name_parent
##    <int> <chr>                  <chr>      
##  1     1 Technic                Technic    
##  2     3 Competition            Technic    
##  3     4 Expert Builder         Technic    
##  4    16 RoboRiders             Technic    
##  5    17 Speed Slammers         Technic    
##  6    18 Star Wars              Technic    
##  7    19 Supplemental           Technic    
##  8    20 Throwbot Slizer        Technic    
##  9    21 Universal Building Set Technic    
## 10    22 Creator                Creator    
## # ℹ 458 more rows

Sets

Sets sum of rows with NA value

## [1] 0

Correlation between year and num_parts

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Join sets with prepared themes

## # A tibble: 21,880 × 4
##     year num_parts name_child     name_parent
##    <int>     <int> <chr>          <chr>      
##  1  1965        43 Technic        Technic    
##  2  1979        12 Classic Town   Town       
##  3  1987         0 Lion Knights   Castle     
##  4  1979        12 Supplemental   Space      
##  5  1979        12 Supplemental   Space      
##  6  1979         2 Supplemental   Space      
##  7  1979        18 Supplemental   Space      
##  8  1979        15 Classic Castle Castle     
##  9  1965         3 Technic        Technic    
## 10  2013         4 Books          Books      
## # ℹ 21,870 more rows

156 Parent themes with number of sets and sum of parts

## # A tibble: 156 × 3
##    name_parent     count_sets sum_parts
##    <chr>                <int>     <int>
##  1 4 Juniors               54      2317
##  2 Adventurers             83      8458
##  3 Agents                  28     11970
##  4 Alpha Team              31      4313
##  5 Angry Birds              6      2388
##  6 Animal Crossing          5         0
##  7 Aquazone                37      6577
##  8 Architecture            60     39346
##  9 Atlantis                24      5323
## 10 Avatar                   9      5522
## # ℹ 146 more rows

Top 10 themes based on number of sets

## # A tibble: 10 × 3
##    name_parent             count_sets sum_parts
##    <chr>                        <int>     <int>
##  1 Gear                          3265      8653
##  2 Duplo                         1293     45232
##  3 Books                          981      8007
##  4 Star Wars                      948    348777
##  5 City                           870    183107
##  6 Collectible Minifigures        858      6025
##  7 Technic                        804    272966
##  8 Educational and Dacta          678    127674
##  9 Town                           677     84587
## 10 Friends                        561    121489

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

More analysis (interactive + animations)

Trends

Forecasting